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International Journal of Informatics and Communication Technology (IJ-ICT)
Published by Institute of Advanced Engineering and Science
ISSN : 22528776     EISSN : 27222616     DOI : -
Core Subject : Science,
Computer Science & IT
International Journal of Informatics and Communication Technology (IJ-ICT) is a common platform for publishing quality research paper as well as other intellectual outputs. This Journal is published by Institute of Advanced Engineering and Science (IAES) whose aims is to promote the dissemination of scientific knowledge and technology on the Information and Communication Technology areas, in front of international audience of scientific community, to encourage the progress and innovation of the technology for human life and also to be a best platform for proliferation of ideas and thought for all scientists, regardless of their locations or nationalities. The journal covers all areas of Informatics and Communication Technology (ICT) focuses on integrating hardware and software solutions for the storage, retrieval, sharing and manipulation management, analysis, visualization, interpretation and it applications for human services programs and practices, publishing refereed original research articles and technical notes. It is designed to serve researchers, developers, managers, strategic planners, graduate students and others interested in state-of-the art research activities in ICT.
Arjuna Subject : -
Articles 10 Documents
 1 
Search results for , issue "Vol 10, No 3: December 2021" : 10 Documents clear
Overview of EEG Signal Processing for Brain Response to Stimuli Ravindra, Bhat
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 10, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v10i3.pp%p

Abstract

The brain is one of the most complicated organs in the human body that controls the entire actions/reactions of the body by getting diverse stimuli via the nervous system. The stimulus that is stronger than the threshold stimulus is decoded by the sensory neurons counts creating information on the frequency and the stimulus of the action potentials. This work intends to plan a detailed survey on brain response to stimuli in EEG signal processing by reviewing 35 papers to determine the shortcoming of contributed works. The analysis is exploited in terms of chronological review, and algorithmic analysis. This analysis determines the utilization of diverse models/approaches in the contributed papers. Moreover, the performance parameter analysis along with the best performance is as well stated clearly. Finally, the research gaps and challenges that rely on this topic are clearly described that paves the way for future research contributions.
Knowledge and utilization of health informatics among medical doctors in Ahmadu Bello University Teaching Hospital, Shika-Zaria Jamila Mohammed Dahiru
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 10, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v10i3.pp171-181

Abstract

The strategic visions in the health care system now underpin application of Information Communication Technology for effective care delivery. Recognising the potentials of ICT for Health, the Nigerian government as part of its policy derive towards achieving effective health care delivery by the year 2020, adopted use of ICT for effective healthcare delivery. Part of the target was establishment of ICT/Health informatics units in teaching hospitals in the country. This study assessed level of knowledge and utilization of health informatics among medical doctors in Ahmadu Bello University, Shika-Zaria. Premised on the Unified Theory of Acceptance and Use of Technology, this study adopted descriptive survey method and structured questionnaire as its instrument of data collection. Findings revealed that 91.4% (n=201) of the 220 sampled medical doctors in ABUTH are aware of health informatics and that 68.2% (n=150) have knowledge of how to use it. The most prominent areas of health informatics among the medical doctors are ‘Management Information System’, ‘Electronic Health Record System’ and ‘Electronic Medical Record’. However, only 58.6% (n=129) of the medical doctors in BUTH Shika- Zaria are actually utilizing health informatics. Internet (n=133, 60.5%) and interpersonal sources (n= 71, 32.3%) were found to be the major sources of information of knowledge and utilization of health informatics among the medical doctors. The study therefore recommends that ABUTH, Shika-Zaria should put in place more mechanism for creating awareness on use of health informatics varieties and a strong need for time-series research to examine future development in respect to knowledge and utilization of health informatics in Nigerian health institutions.
Electronic health record to predict a heart attack used data mining with Naïve Bayes method Johanes Fernandes Andry; Fabio Mangatas Silaen; Hendy Tannady; Kevin Hadi Saputra
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 10, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v10i3.pp182-187

Abstract

A heart attack is a medical emergency. A heart attack usually occurs when a blood clot blocks the flow of blood to the heart. Cardiovascular disease is a variety of diseases that attack the body's cardiovascular system including the heart and blood vessels. Cardiovascular diseases (CVD) include angina, arrhythmia, heart attack, heart failure, atherosclerosis, stroke, and so on. To resolving (CVD) is to evaluate large scores of datasets, to compare for any information that can be used to forecast, to take care of organize. The method used Naïve Bayes classification because that method can determine target which can be used to answer some questions like whether the patient has the potential for heart disease. After data analyst, authors can use data to electronic health records (EHR).
An efficient coverage and maximization of network lifetime in wireless sensor networks through metaheuristics A. Nageswar Rao; B. Rajendra Naik; L. Nirmala Devi
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 10, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v10i3.pp159-170

Abstract

In wireless sensor networks (WSNs), energy, connectivity, and coverage are the three most important constraints for guaranteed data forwarding from every sensor node to the base station. Due to continuous sensing and transmission tasks, the sensor nodes deplete more quickly and hence they seek the help of data forwarding nodes, called relay nodes. However, for a given set of sensor nodes, finding optimal locations to place relay nodes is a very challenging problem. Moreover, from the earlier studies, the relay node placement is defined as a non-deterministic polynomial tree hard (NP-Hard) problem. To solve this problem, we propose a multi-objective firefly algorithm-based relay node placement (MOFF-RNP) to deploy an optimal number of relay nodes while considering connectivity, coverage, and energy constraints. To achieve network lifetime, this work adopted energy harvesting capabilities to the sensor nodes and backup relay strategy such that every sensor node is always connected to at least one relay to forward the data. The optimal relay placement is formulated as an objective function and MOFF is applied to achieve a better solution. Extensive Simulations are carried out over the proposed model to validate the performance and the obtained results are compared with state-of-art methods)
Forensic steganalysis for identification of steganography software tools using multiple format image S. T. Veena; A. Selvaraj
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 10, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v10i3.pp188-197

Abstract

Today many steganographic software tools are freely available on the Internet, which helps even callow users to have covert communication through digital images. Targeted structural image steganalysers identify only a particular steganographic software tool by tracing the unique fingerprint left in the stego images by the steganographic process. Image steganalysis proves to be a tough challenging task if the process is blind and universal, the secret payload is very less and the cover image is in lossless compression format. A payload independent universal steganalyser which identifies the steganographic software tools by exploiting the traces of artefacts left in the image and in its metadata for five different image formats is proposed. First, the artefacts in image metadata are identified and clustered to form distinct groups by extended K-means clustering. The group that is identical to the cover is further processed by extracting the artefacts in the image data. This is done by developing a signature of the steganographic software tool from its stego images. They are then matched for steganographic software tool identification. Thus, the steganalyser successfully identifies the stego images in five different image formats, out of which four are lossless, even for a payload of 1 byte. Its performance is also compared with the existing steganalyser software tool.
On the Evaluation and Implementation of LSTM Model for Speech Emotion Recognition using MFCC Bhandari, Sheetal
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 10, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v10i3.pp%p

Abstract

Speech Emotion Recognition is an emerging research field and is expected to benefit many application domains by providing effective Human Computer Interface. Researchers are extensively working towards decoding of human emotions through speech signal in order to achieve effective interface and smart response by computers. The perfection of speech emotion recognition greatly depends upon the types of features used and also on the classifier employed for recognition. The contribution of this paper is to evaluate twelve different Long Short Term Memory (LSTM) networks models as classifier based on Mel-Frequency Cepstrum Coefficients (MFCC) feature. The paper presents performance evaluation in terms of important parameters such as: precision, recall, F-measure and accuracy for four emotions like happy, neutral, sad and angry using the emotional speech databases namely Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). The measurement accuracy obtained is 89% which is 9.5% more than reported in recent literature. The suitable LSTM model is further successfully implemented on Raspberry PI board creating standalone Speech Emotion Recognition system.
Narrow-band filter for satellite communication systems Alexander Vladimirovich Strizhachenko; Sergey Nikolayevich Shulga
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 10, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v10i3.pp198-203

Abstract

Design narrow-band compact filters, based on high-quality waveguide-dielectric resonators with anisotropic materials is the subject of this paper. Filter represents a segment of a rectangular waveguide rotated around the longitudinal axis of the waveguide 90 degrees and containing one or more dielectric inserts that completely fill the resonator along the narrow wall of the waveguide and partially along the wide one. A distinctive feature of the proposed filter is higher slope steepness of the amplitude-frequency characteristic, and high manufacturability in the centimeter range. The designed narrow-band filter satisfies contradictory requirements: it combines narrow bandwidth (≈ 0.1% of center frequency f0) with low passband insertion loss (≤ 1 dB).
An approach to partial occlusion using deep metric learning Chethana Hadya Thammaiah; Trisiladevi Chandrakant Nagavi
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 10, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v10i3.pp204-211

Abstract

The human face can be used as an identification and authentication tool in biometric systems. Face recognition in forensics is a challenging task due to the presence of partial occlusion features like wearing a hat, sunglasses, scarf, and beard. In forensics, criminal identification having partial occlusion features is the most difficult task to perform. In this paper, a combination of the histogram of gradients (HOG) with Euclidean distance is proposed. Deep metric learning is the process of measuring the similarity between the samples using optimal distance metrics for learning tasks. In the proposed system, a deep metric learning technique like HOG is used to generate a 128d real feature vector. Euclidean distance is then applied between the feature vectors and a tolerance threshold is set to decide whether it is a match or mismatch. Experiments are carried out on disguised faces in the wild (DFW) dataset collected from IIIT Delhi which consists of 1000 subjects in which 600 subjects were used for testing and the remaining 400 subjects were used for training purposes. The proposed system provides a recognition accuracy of 89.8% and it outperforms compared with other existing methods.
Heart disease prediction model with k-nearest neighbor algorithm Tssehay Admassu Assegie
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 10, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v10i3.pp225-230

Abstract

In this study, the author proposed k-nearest neighbor (KNN) based heart disease prediction model. The author conducted an experiment to evaluate the performance of the proposed model. Moreover, the result of the experimental evaluation of the predictive performance of the proposed model is analyzed. To conduct the study, the author obtained heart disease data from Kaggle machine learning data repository. The dataset consists of 1025 observations of which 499 or 48.68% is heart disease negative and 526 or 51.32% is heart disease positive. Finally, the performance of KNN algorithm is analyzed on the test set. The result of performance analysis on the experimental results on the Kaggle heart disease data repository shows that the accuracy of the KNN is 91.99%
ZigBee based data collection in wireless sensor networks Cuong V. Nguyen; Alberto E. Coboi; Nam V. Bach; Anh TN. Dang; Trang TH. Le; Huy P. Nguyen; Minh Tuan Nguyen
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 10, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v10i3.pp212-224

Abstract

Wireless sensor networks (WSN), referring to groups of technologies wirelessly controlled, are widely used in many different fields, agriculture, medical, military, etc. These technologies are mainly used for monitoring physical or environmental conditions, such as temperatures, sound, pressure, and so on. In WSN fields, there are technologies as Wi-Fi, radio frequency (RF), Bluetooth, ZigBee, Z-Wave, and so on. Furthermore, there is one of this technology that offers more outstanding futures to provide more energy-saving and long distances of transmissions compared to other technologies, and that is Zigbee technology, and this had become for many applications, the first high-quality to use and consequently the most used in WSNs. In Zigbee aided WSNs, are included three main devices used to communicate data, that is a Zig-Bee coordinator (network coordinator), ZigBee router, and ZigBee end-devices. The data sensed is transmitted from sensor nodes through coordinators to a base-station (BS), this device (coordinator), collects the data, stores it in a memory, processes, and finally forward to the next suitable nodes or the BS. This research presents the concepts and discussions of Zigbee technologies used in WSNs. Utmost ZigBee communication technologies are revised and analyzed, as well as simulation results with different scenarios are addressed comprehensively. Proposals for advance applications in WSNs are presented. Suggestions for future developments are provided

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